Advances in microprocessor technology have enabled the application of modern control techniques and failure detection diagnostics to various processes for improved system performance. This paper presents experimental results for an on-board microprocessor-based failure detection package designed to assist in the diagnosis of heat pump failures. A model-free limit and trend checking scheme, and a model-based innovations detection formulation operate in parallel to detect anomalous behavior. This dual approach permits the study of tradeoffs between failure detection performance and method complexity. A series of typical anomalies are experimentally simulated in a heat pump, and results are presented to demonstrate the performance of each detection strategy.

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